Update README.md

README.md

@@ -284,17 +284,4 @@ print(outputs[0]["generated_text"])
 
 Output:
 
-> A Mixture of Experts (ME) is a machine learning technique that combines multiple expert models to make predictions or decisions. Each expert model is specialized in a different aspect of the problem, and their outputs are combined to produce a more accurate and robust solution. This approach allows the model to leverage the strengths of individual experts and compensate for their weaknesses, improving overall performance.
-# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
-Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mlabonne__Beyonder-4x7B-v2)
-
-|             Metric              |Value|
-|---------------------------------|----:|
-|Avg.                             |72.33|
-|AI2 Reasoning Challenge (25-Shot)|68.77|
-|HellaSwag (10-Shot)              |86.80|
-|MMLU (5-Shot)                    |65.10|
-|TruthfulQA (0-shot)              |60.68|
-|Winogrande (5-shot)              |80.90|
-|GSM8k (5-shot)                   |71.72|
-
+> A Mixture of Experts (ME) is a machine learning technique that combines multiple expert models to make predictions or decisions. Each expert model is specialized in a different aspect of the problem, and their outputs are combined to produce a more accurate and robust solution. This approach allows the model to leverage the strengths of individual experts and compensate for their weaknesses, improving overall performance.